LionGuard: Building a Contextualized Moderation Classifier to Tackle Localized Unsafe Content
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866913436521201664 |
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| author | Foo, Jessica Khoo, Shaun |
| author_facet | Foo, Jessica Khoo, Shaun |
| contents | As large language models (LLMs) become increasingly prevalent in a wide variety of applications, concerns about the safety of their outputs have become more significant. Most efforts at safety-tuning or moderation today take on a predominantly Western-centric view of safety, especially for toxic, hateful, or violent speech. In this paper, we describe LionGuard, a Singapore-contextualized moderation classifier that can serve as guardrails against unsafe LLM outputs. When assessed on Singlish data, LionGuard outperforms existing widely-used moderation APIs, which are not finetuned for the Singapore context, by 14% (binary) and up to 51% (multi-label). Our work highlights the benefits of localization for moderation classifiers and presents a practical and scalable approach for low-resource languages. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_10995 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LionGuard: Building a Contextualized Moderation Classifier to Tackle Localized Unsafe Content Foo, Jessica Khoo, Shaun Computation and Language Artificial Intelligence As large language models (LLMs) become increasingly prevalent in a wide variety of applications, concerns about the safety of their outputs have become more significant. Most efforts at safety-tuning or moderation today take on a predominantly Western-centric view of safety, especially for toxic, hateful, or violent speech. In this paper, we describe LionGuard, a Singapore-contextualized moderation classifier that can serve as guardrails against unsafe LLM outputs. When assessed on Singlish data, LionGuard outperforms existing widely-used moderation APIs, which are not finetuned for the Singapore context, by 14% (binary) and up to 51% (multi-label). Our work highlights the benefits of localization for moderation classifiers and presents a practical and scalable approach for low-resource languages. |
| title | LionGuard: Building a Contextualized Moderation Classifier to Tackle Localized Unsafe Content |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2407.10995 |